I had my first Covid jab last week, the Astra Zeneca/Oxford vaccine, and wanted to know more about the people and process behind the incredible endeavour. It has all the hallmarks of a great startup story – passionate people, experimentation, testing, bad news, breakthroughs, pivots, collaboration – and success.
The history of vaccines goes back a thousand years. C7th Indian Buddhist monks were said to drink snake venom to make them immune from bites. In the UK, Edward Jenner, the C18th physician after whom the Oxford institute is named, pioneered the ‘attenuated’ vaccine by inoculating an eight-year-old boy with cowpox, a mild virus from the same group of viruses as smallpox, to give him immunity. Nowadays what was once a matter of trial and error is a structured scientific process.
The Oxford Vaccine Group has spent the last year developing a life-saving vaccine against Covid-19. The story of how the vaccine was developed and rolled out at world-beating speed is extraordinary. The Oxford team achieved what had never been attempted before: the creation, testing, manufacture and distribution of a vaccine at the same time as a pandemic raged.
Professor Sarah Gilbert, a vaccinologist, was at home on New Year’s Day 2019, looking at a website dedicated to infectious disease outbreaks. A report on an illness in the Chinese city of Wuhan caught her attention: an unknown virus. Might that cause the next pandemic? Was this it?
It was. Could her team develop a vaccine? Yes, but before trying to develop a solution, she and her colleagues needed to know the genetic code of the virus. However, China had told its scientists not to publicise data and had failed to acknowledge the human-to-human transfer of the virus, let alone the threat it might pose to the world.
On January 11, Teresa Lambe, one of Gilbert’s colleagues, was awoken by a ping on her phone: Zhang Yongzhen, a determined Chinese researcher, had sent her the virus’s genetic code. Checking it out, Lambe and Gilbert were confident they could create a vaccine.
By March, Gilbert’s team had made a first batch of vaccine. Testing on humans began in April, with 10,000 volunteers. Two Oxford postdoc researchers — Elisa Granato, and Edward O’Neill, were the first to receive the jab. Over the next two days the team checked that their human guinea pigs had not suffered ill effects before vaccinating six more volunteers.
The team accelerated testing. Now speed was essential. Nobody had planned how to develop a pandemic vaccine during a pandemic. That was something they had to do. The big question was: How do we go really quickly? What’s the fastest route to get into clinical development? The problem in April, though, was how to make enough of it, so they outsourced production to Italy. But by the time the first Italian batch was ready for delivery, a European-wide lockdown meant scheduled flights were grounded, so they had to charter a plane to get the extra doses home.
That wasn’t the only hurdle. The Italians had used a different technique for measuring the concentration of the vaccine, which led to a miscalculation. Volunteers ended up being given what was discovered later to be half a dose rather than a full one. Yet it didn’t seem to matter, the results suggested that a half dose followed by a full one worked better than two full doses. In fact, production delays had meant a longer gap between doses turned out to be a blessing. Scientists believe it was not the half dose that made the vaccine more effective but the longer interval between doses.
While puzzling over the science, Gilbert realised that if they were to produce enough vaccine, they would need a commercial partner, and one who would not exploit for profit. Another condition was that production had to start immediately, before it was known if the vaccine worked. They held discussions with several manufacturers until April 30, when AstraZeneca agreed to a partnership.
Further research results continued to be positive, but on September 6 the Oxford team were forced to halt trials when a participant developed a rare neurological condition. The review did not find any reason to suspect the vaccine was at fault, and within days regulators said the trials could resume.
Oxford’s full trial results, released on November 23, were complicated. The vaccine was 90% effective on the volunteers who were given the half dose followed by a full dose four weeks later, but less so (62%) on a larger group that got the two full doses. The average effectiveness was 70%.
Then came the moment the Oxford team had been waiting for. On December 30 their vaccine was authorised for use in the UK, and mass vaccinations started. Whilst a ‘South African’ variant was detected, for the scientists, it’s just another puzzle – and they’ve had a lot of those along the way – whatever the coming months bring, the team that produced a vaccine at such speed and under such pressure won’t be forgotten.
You can see that the Oxford researchers resemble a nimble startup, a team of entrepreneurial thinkers: innovation mindsets, agility in experimentation, motivation to think and act quickly using their human capital to solve problems. In startup terms, their research process followed the Lean Startup methodology.
The Lean Startup is a structured process framing an experimental mindset, guiding entrepreneurs from a ‘just do it’ focus on seeking the right answers to asking the right questions. This captures economist John Maynard Keynes’ sentiment of it’s better to be roughly right, than precisely wrong in your business thinking. The aim is to drive the development of a product by reducing uncertainty. It starts by establishing your initial hypothesis around answering the question: What is the problem you are trying to solve?
Articulating what they think they know about the problem, and what it is they want to learn next, the approach adopts the Build-Measure-Learn feedback loop of experimentation, discovery and evaluation. In other words, think first about what you already know, then form a testable hypothesis to validate, and then move on to building and measuring.
It matters where you start because building without some reasonable understanding of your users’ needs is just guessing, and if you’re guessing, you’re in the realm of betting rather than science. Every time this happens, you lose an opportunity to validate and improve your understanding of your users, and you waste time and money.
Writing a good hypothesis is difficult, but makes it easier to design an experiment to test and learn from. A proper hypothesis is an educated guess about your problem and potential users. How do you do this? Here is a template for crafting hypotheses. I’ll start with this simple example:
Because we believe a, if we do b, we expect c to happen.
That’s no different to the Oxford scientists seeking the vaccine formula and impact.
A hypothesis is essentially a statement of belief that expresses why you think your innovation, or change to your product or service, will create value. The hypothesis is what you test when you run experiments, to try and turn that belief into more certain knowledge.
For example, Amazon was created on the belief that people would be happy to buy books online. Similarly, smartphones were created on the belief that users would be willing to pay a premium for a phone that offered additional functionality other than calls and texts.
Writing a hypothesis like this means we must assert something about what we know or have already learned that we can then validate. This is true validated learning. Let’s add more detail using a micro-brewery artisan beer business as an example and get one step closer to having a hypothesis we can validate:
We believe our customers prefer stronger hoppy, hazy IPA in warmer weather; we will test this if we add a lager yeast and make the beer unfiltered and unpasteurised, it will taste better chilled; if we are right, we expect higher customer satisfaction and more sales.
If you are correct, you might run further tests to find the quantity of yeast and overall fermentation process to influence the taste of the IPA. that maximises sales. Equally, if we are wrong, it might indicate that customer preference for chilled IPA might be influenced by other factors such as the precise ratio of hops, the exact temperature outside, the quantity of sugar, and water in our recipe, or the price of a drink from an alternative micro-brewery down the street.
Even this simple example highlights many factors that might affect experiment design and results interpretation. Taking time to think, articulate what you know, and expose your assumptions while forming a proper, testable hypothesis will help you do truly validated learning, and help your next step.
So how do you create and articulate a good hypothesis? Here are five steps to consider when formulating your hypothesis.
1. Focus You are unlikely to be able to test your entire innovation with one hypothesis, so consider the two key ones: the value hypothesis and the growth hypothesis:
- The value hypothesis is designed to test whether your product or service provides potential customers with enough value once they are using it and therefore, whether they would be willing to pay for it.
- The growth hypothesis tests how customers will find and start using your new product. If you have assumed that you can grow your customer base through your wen site, testing how many users go to your web site will give you an idea of whether that is likely to work.
2. Keep it simple There are many ways of structuring your hypotheses, so find the one that works for you. I’d recommended starting your hypothesis with the phrase We believe… to articulate your assumptions clearly, and one approach is to follow this up with We will test this by…and We are right if…These three simple phrases enable you to include the basic elements of a good hypothesis without overcomplicating matters.
3. Start with what you know As highlighted above, I think the best way to start constructing a hypothesis is with the phrase We believe…This allows us to express clearly our assumptions and ensure that it is tied to what you know about your customers. If you are unable to clearly articulate these foundation assumptions, it’s better to pause, and develop a better understanding of your customers than running haphazard experiments
4. Have a metric How will you know if your hypothesis is correct? You need a metric. There are two aspects to making your hypothesis measurable: choosing the right metric and setting a clear objective.
The metric you choose should be tied to what you are testing: for example, if you are testing the value hypothesis, asking potential customers if they would pay is not as reliable as building an MVP and seeing if they actually do. Think hard about what metric will give you the most useful information. Similarly, be precise about your objective. Testing whether customers will pay for your product is one thing but finding out if enough customers will pay for it is more valuable.
5. Set a timeframe So, you’ve decided that you need one hundred volunteers in a month to use your initial vaccine to prove your hypothesis. But over the course of a month, is a hundred and is a month enough to prove your hypothesis? Is the feedback timely enough? Be clear about your timeframe – how long will you run the experiment in order to generate useful results, and what do you expect to happen in that timeframe?
The NASA Apollo lunar programme was the first ‘moonshot’ – literally, but also a term which came to capture a type of extremely challenging endeavour, one that is a noble pursuit. It is an act of ambition that inspires people to go beyond what they think possible.
Creating breakthroughs, asking searching what if? questions about when and how something innovative and bold can be accomplished is true entrepreneurial thinking. But it’s not just about ‘giving it a go’, you need some research and facts at hand to validate your thinking, and the Lean Startup process with its anchor in hypothesis provides this ‘disciplined entrepreneurship’.
The Oxford vaccine team has been a true moonshot. Take inspiration from ‘this has never been done before’ and go for your own moonshot startup venture today, and make your mark.